input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
AIML_API = "aiml_api"
ANTHROPIC = "anthropic"
APOLLO = "apollo"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GENERIC_WEBHOOK = "generic_webhook"
G... | from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
AIML_API = "aiml_api"
ANTHROPIC = "anthropic"
APOLLO = "apollo"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GENERIC_WEBHOOK = "generic_webhook"
G... |
from __future__ import annotations
__version__ = "3.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... | from __future__ import annotations
__version__ = "3.4.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... |
from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... | from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... |
import itertools
import numpy as np
from keras.src import tree
from keras.src.trainers.data_adapters import data_adapter_utils
from keras.src.trainers.data_adapters.data_adapter import DataAdapter
class TorchDataLoaderAdapter(DataAdapter):
"""Adapter that handles `torch.utils.data.DataLoader`."""
def __ini... | import itertools
import numpy as np
from keras.src import tree
from keras.src.trainers.data_adapters import data_adapter_utils
from keras.src.trainers.data_adapters.data_adapter import DataAdapter
class TorchDataLoaderAdapter(DataAdapter):
"""Adapter that handles `torch.utils.data.DataLoader`."""
def __ini... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.video_t... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.video_t... |
from docarray import DocumentArray
from jina import Executor, requests
class ProcessExecutor(Executor):
@requests(on='/')
def process(self, docs: DocumentArray, **kwargs):
for doc in docs:
doc.text = doc.text + 'world'
doc.tags['processed'] = True
def _validate_dummy_custom_... | def _validate_dummy_custom_gateway_response(port, expected):
import requests
resp = requests.get(f'http://127.0.0.1:{port}/').json()
assert resp == expected
def _validate_custom_gateway_process(port, text, expected):
import requests
resp = requests.get(f'http://127.0.0.1:{port}/stream?text={text... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDocument):
"""
Document for handling text.
It can conta... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDocument):
"""
Document for handling text.
It can conta... |
from __future__ import annotations
import logging
import tqdm
class LoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET) -> None:
super().__init__(level)
def emit(self, record) -> None:
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
... | import logging
import tqdm
class LoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET) -> None:
super().__init__(level)
def emit(self, record) -> None:
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
self.flush()
except (Key... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
import re
from langchain_core.output_parsers import BaseOutputParser
class BooleanOutputParser(BaseOutputParser[bool]):
"""Parse the output of an LLM call to a boolean."""
true_val: str = "YES"
"""The string value that should be parsed as True."""
false_val: str = "NO"
"""The string value that s... | import re
from langchain_core.output_parsers import BaseOutputParser
class BooleanOutputParser(BaseOutputParser[bool]):
"""Parse the output of an LLM call to a boolean."""
true_val: str = "YES"
"""The string value that should be parsed as True."""
false_val: str = "NO"
"""The string value that s... |
from docarray import Document, DocumentArray
import numpy as np
import pytest
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_add_ignore_existing_doc_id(start_storage):
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim': 3,
'columns': [('pri... | from docarray import Document, DocumentArray
import pytest
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_add_ignore_existing_doc_id(start_storage):
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim': 3,
'columns': [('price', 'int')],
... |
"""Load agent."""
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain._api.deprec... | """Load agent."""
from typing import Any, Optional, Sequence
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain._api.deprecation import AGENT_DEPRECAT... |
"""
This examples demonstrates the setup for Question-Answer-Retrieval.
You can input a query or a question. The script then uses semantic search
to find relevant passages in Simple English Wikipedia (as it is smaller and fits better in RAM).
As model, we use: nq-distilbert-base-v1
It was trained on the Natural Ques... | """
This examples demonstrates the setup for Question-Answer-Retrieval.
You can input a query or a question. The script then uses semantic search
to find relevant passages in Simple English Wikipedia (as it is smaller and fits better in RAM).
As model, we use: nq-distilbert-base-v1
It was trained on the Natural Ques... |
from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"... | import json
import os
from typing import List
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"""
def ... |
"""Utilities for the XGBoost Dask interface."""
import logging
import warnings
from typing import Any, Dict, Optional, Tuple
import distributed
from ..collective import Config
LOGGER = logging.getLogger("[xgboost.dask]")
def get_n_threads(local_param: Dict[str, Any], worker: "distributed.Worker") -> int:
"""G... | """Utilities for the XGBoost Dask interface."""
import logging
from typing import TYPE_CHECKING, Any, Dict
LOGGER = logging.getLogger("[xgboost.dask]")
if TYPE_CHECKING:
import distributed
def get_n_threads(local_param: Dict[str, Any], worker: "distributed.Worker") -> int:
"""Get the number of threads fro... |
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically Infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/segmentation/VOCde... | # dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
... |
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Image(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for images.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be turned into a tens... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Image(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for images.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be turned into a tens... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.callbacks.splade_callbacks import (
SchedulerType,
SpladeLambdaSchedulerCallback,
)
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (... | from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import dtype_policies
from keras.src import layers
from keras.src import testing
class ZeroPadding3DTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
{"data_format": "channels_f... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import layers
from keras.src import testing
class ZeroPadding3DTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
{"data_format": "channels_first"}, {"data_format": "channels_las... |
"""Google Universal Sentence Encoder Embedding Wrapper Module."""
import deprecated
from typing import Any, List, Optional
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.callbacks imp... | """Google Universal Sentence Encoder Embedding Wrapper Module."""
from typing import Any, List, Optional
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.callbacks import CallbackManage... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.parsers.generic import MimeTypeBasedParser
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# hand... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.parsers.generic import MimeTypeBasedParser
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# hand... |
from typing import Union
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.image.image_torch_tensor import ImageTorchTensor
tf_available = ... | from typing import Union
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
try:
import torch # noqa: F401
except ImportError:
ImageTensor = ImageNdArray
else:
from docarray.typing.tensor.image.image_torch_tensor import ImageTorchTensor
ImageTensor = Union[ImageNdArray, ImageTorchT... |
"""LLM Prompt Program."""
from abc import abstractmethod
from typing import Any, Generic, Optional, Type, TypeVar
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.types import BasePydanticProgram, Model
LM = TypeVar("LM")
class Ba... | """LLM Prompt Program."""
from abc import abstractmethod
from typing import Any, Generic, Optional, Type, TypeVar
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.types import BasePydanticProgram, Model
LM = TypeVar("LM")
class Bas... |
_base_ = './rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = './rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
|
_base_ = './panoptic-fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
from __future__ import annotations
import inspect
from typing import Any, Union
import torch
try:
import triton
except ImportError:
triton = None
if triton is not None:
import triton.language as tl
from triton import Config
from triton.compiler import CompiledKernel
from triton.runtime.aut... | from __future__ import annotations
import inspect
from typing import Any, Union
import torch
try:
import triton
except ImportError:
triton = None
if triton is not None:
import triton.language as tl
from triton import Config
from triton.compiler import CompiledKernel
from triton.runtime.aut... |
from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .LambdaLoss import (
LambdaLoss,
LambdaRankScheme,
NDCGLoss1Scheme,
NDCGLo... | from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .MarginMSELoss import MarginMSELoss
from .MSELoss import MSELoss
from .MultipleNegativesRa... |
_base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
# use caffe img_norm
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(req... | _base_ = './retinanet_r50_fpn_1x_coco.py'
# use caffe img_norm
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_ev... |
from enum import Enum
from fsspec import AbstractFileSystem
from pathlib import Path
from typing import Any, Dict, Iterable, Optional, Protocol, runtime_checkable
import json
import uuid
from docling.document_converter import DocumentConverter
from docling_core.types import DoclingDocument as DLDocument
from llama_ind... | from enum import Enum
from fsspec import AbstractFileSystem
from pathlib import Path
from typing import Any, Dict, Iterable, Optional, Protocol, runtime_checkable
import json
import uuid
from docling.document_converter import DocumentConverter
from docling_core.types import DoclingDocument as DLDocument
from llama_ind... |
# mypy: allow-untyped-defs
import torch._C._lazy
def reset():
"""Resets all metric counters."""
torch._C._lazy._reset_metrics()
def counter_names():
"""Retrieves all the currently active counter names."""
return torch._C._lazy._counter_names()
def counter_value(name: str):
"""Return the value ... | # mypy: allow-untyped-defs
import torch._C._lazy
def reset():
"""Resets all metric counters."""
torch._C._lazy._reset_metrics()
def counter_names():
"""Retrieves all the currently active counter names."""
return torch._C._lazy._counter_names()
def counter_value(name: str):
"""Return the value ... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, List, Optional, Tuple
import numpy as np
import torch
import torchvision.transforms as T
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogge... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Dict, Iterable, Tuple
import numpy as np
import torchvision.transforms as T
import torch
from jina import Executor, requests, DocumentArray
from jina_commons.batching import get_d... |
import os
from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CBEventType, EventPayload
from llama_index.core.instrumentation import get_dispatcher
from llama_index.core.instrumentation.events.rerank import (
ReRankEndEvent,
... | import os
from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CBEventType, EventPayload
from llama_index.core.instrumentation import get_dispatcher
from llama_index.core.instrumentation.events.rerank import (
ReRankEndEvent,
... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import numpy as np
from docarray import BaseDoc, DocList
from docarray.index import InMemoryExactNNIndex
from docarray.typing import NdArray
class SimpleDoc(BaseDoc):
embedding: NdArray[128]
text: str
def test_update_payload():
docs = DocList[SimpleDoc](
[SimpleDoc(embedding=np.random.rand(128)... |
from abc import abstractmethod
from typing import Iterable, Iterator
from qdrant_client import QdrantClient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models.models import (
PointIdsList,
PointsList,
ScrollRequest,
PointStruct,
)
from docarray import Document
... | from abc import abstractmethod
from typing import Iterable, Iterator
from qdrant_client import QdrantClient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models.models import (
PointIdsList,
PointsList,
ScrollRequest,
PointStruct,
)
from docarray import Document
... |
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
from keras.src.ops import convert_to_tensor
class StringLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
... | import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class StringLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
def test_config(self):
layer = laye... |
import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Value
from datasets.packaged_modules.json.json import Json
@pytest.fixture
def jsonl_file(tmp_path):
filename = tmp_path / "file.jsonl"
data = textwrap.dedent(
"""\
{"col_1": -1}
{"col_1": 1, "col_2": 2... | import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Value
from datasets.packaged_modules.json.json import Json
@pytest.fixture
def jsonl_file(tmp_path):
filename = tmp_path / "file.jsonl"
data = textwrap.dedent(
"""\
{"col_1": 1, "col_2": 2}
{"col_1": 10... |
# Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... | # Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... |
__version__ = '0.1.0'
from docarray.array import DocumentArray
from docarray.document.document import BaseDocument as Document
from docarray.predefined_document import Image, Mesh3D, PointCloud3D, Text
__all__ = ['Document', 'DocumentArray', 'Image', 'Text', 'Mesh3D', 'PointCloud3D']
| __version__ = '0.1.0'
from docarray.array import DocumentArray
from docarray.document.document import BaseDocument as Document
from docarray.predefined_document import Image, Text
__all__ = ['Document', 'DocumentArray', 'Image', 'Text']
|
import re
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS
class ConvoOutputParser(AgentOutputPar... | import re
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS
class ConvoOutputParser(AgentOutputPar... |
import torch
_TORCHFUNCTION_SUBCLASS = False
class _ReturnTypeCM:
def __init__(self, to_restore):
self.to_restore = to_restore
def __enter__(self):
return self
def __exit__(self, *args):
global _TORCHFUNCTION_SUBCLASS
_TORCHFUNCTION_SUBCLASS = self.to_restore
def set_r... | import torch
_TORCHFUNCTION_SUBCLASS = False
class _ReturnTypeCM:
def __init__(self, to_restore):
self.to_restore = to_restore
def __enter__(self):
return self
def __exit__(self, *args):
global _TORCHFUNCTION_SUBCLASS
_TORCHFUNCTION_SUBCLASS = self.to_restore
def set_r... |
from ._hubert_datamodule import HuBERTDataModule
__all__ = [
"HuBERTDataModule",
"Wav2Vec2DataModule",
]
| from ._hubert_datamodule import HuBERTDataModule
__all__ = [
"HuBERTDataModule",
]
|
"""**Callback handlers** allow listening to events in LangChain.
**Class hierarchy:**
.. code-block::
BaseCallbackHandler --> <name>CallbackHandler # Example: AimCallbackHandler
"""
from langchain_core.callbacks.base import (
AsyncCallbackHandler,
BaseCallbackHandler,
BaseCallbackManager,
Callb... | """**Callback handlers** allow listening to events in LangChain.
**Class hierarchy:**
.. code-block::
BaseCallbackHandler --> <name>CallbackHandler # Example: AimCallbackHandler
"""
from langchain_core.callbacks.base import (
AsyncCallbackHandler,
BaseCallbackHandler,
BaseCallbackManager,
Callb... |
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='TestTimeAug',
transforms=[
... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
... |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import NonLocal2d
from mmcv.runner import BaseModule
from mmdet.registry import MODELS
@MODELS.register_module()
class BFP(BaseModule):
"""BFP (Balanced Feature Pyramids)
BFP ... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import NonLocal2d
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class BFP(BaseModule):
"""BFP (Balanced Feature Pyramids)
BFP takes m... |
_base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# dataset settings
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
train_pip... | _base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# dataset settings
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
train_pip... |
"""Matrix decomposition algorithms.
These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be
regarded as dimensionality reduction techniques.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ..utils.extmath import randomized_svd
from ._dict_learning i... | """Matrix decomposition algorithms.
These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be
regarded as dimensionality reduction techniques.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ..utils.extmath import randomized_svd
from ._dict_learning i... |
import numpy as np
import pytest
import torch
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
NdArrayEmbedding,
TorchEmbedding,
)
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.typing import... | import numpy as np
import torch
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
NdArrayEmbedding,
TorchEmbedding,
)
def test_torch_tensors_interop():
t1 = AudioTorchTensor(torch.rand(128))
t2 = TorchEmbedding(torch.rand(128))
t_result = t1 + t2
assert isinstance(t_resul... |
from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from .ContrastiveLoss import SiameseDistanceMetric
from sentence_transformers.SentenceTransformer import SentenceTransformer
class OnlineContrastiveLoss(nn.Module):
def __init__(
self, model: SentenceTransformer... | from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from .ContrastiveLoss import SiameseDistanceMetric
from sentence_transformers.SentenceTransformer import SentenceTransformer
class OnlineContrastiveLoss(nn.Module):
def __init__(
self, model: SentenceTransformer... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
def get_max_cuda_memory(device: Optional[torch.device] = None) -> int:
"""Returns the maximum GPU memory occupied by tensors in megabytes (MB) for
a given device. By default, this returns the peak allocated memory since
... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
def get_max_cuda_memory(device: Optional[torch.device] = None) -> int:
"""Returns the maximum GPU memory occupied by tensors in megabytes (MB) for
a given device. By default, this returns the peak allocated memory since
... |
from keras.src import activations
from keras.src import constraints
from keras.src import initializers
from keras.src import regularizers
from keras.src.api_export import keras_export
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
@keras_export("keras.layers.PReLU")
class P... | from keras.src import activations
from keras.src import constraints
from keras.src import initializers
from keras.src import regularizers
from keras.src.api_export import keras_export
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
@keras_export("keras.layers.PReLU")
class P... |
from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset
class Flowers102(VisionDataset):
"""`Oxford 102 Flower <https://www.robots.ox.ac.uk/~vgg/da... | from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset
class Flowers102(VisionDataset):
"""`Oxford 102 Flower <https://www.robots.ox.ac.uk/~vgg/da... |
from jina.parsers.helper import add_arg_group
def mixin_head_parser(parser):
"""Mixing in arguments required by head pods and runtimes into the given parser.
:param parser: the parser instance to which we add arguments
"""
gp = add_arg_group(parser, title='Head')
gp.add_argument(
'--comp... | from jina.parsers.helper import add_arg_group
def mixin_head_parser(parser):
"""Mixing in arguments required by head pods and runtimes into the given parser.
:param parser: the parser instance to which we add arguments
"""
gp = add_arg_group(parser, title='Head')
gp.add_argument(
'--comp... |
from typing_extensions import TYPE_CHECKING
from docarray.typing.bytes import AudioBytes, ImageBytes, VideoBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray, AudioTensor
from docarray.typing.tensor.embedding.embedd... | from typing import (
Union,
TYPE_CHECKING,
TypeVar,
Sequence,
Optional,
List,
Dict,
Generator,
Iterable,
Tuple,
ForwardRef,
)
if TYPE_CHECKING: # pragma: no cover
import scipy.sparse
import tensorflow
import torch
import numpy as np
from PIL.Image import... |
import collections
import json
import os
import string
from typing import Iterable, List
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
Punctuation are stripped from t... | from typing import Union, Tuple, List, Iterable, Dict
import collections
import string
import os
import json
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
Punctuation a... |
__copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Any, Iterable, Optional
import librosa as lr
import numpy as np
import torch
from jina import DocumentArray, Executor, requests
from jina.excepts import BadDocType
from .audio_clip.model impo... | __copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Any, Iterable, Optional
import librosa as lr
import numpy as np
import torch
from jina import DocumentArray, Executor, requests
from jina.excepts import BadDocType
from .audio_clip.model impo... |
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Image(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for images.
.. note::
In the :ref:`transforms <transforms>`, ``Image`` instances are largely
... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Image(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for images.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be turned into a tens... |
import os
import subprocess
import pytest
from xgboost import testing as tm
pytestmark = [
pytest.mark.skipif(**tm.no_dask()),
pytest.mark.skipif(**tm.no_dask_cuda()),
tm.timeout(60),
]
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.mgpu
def test_dask_training():
script = os.path.join(tm.demo_dir... | import os
import subprocess
import pytest
from xgboost import testing as tm
pytestmark = [
pytest.mark.skipif(**tm.no_dask()),
pytest.mark.skipif(**tm.no_dask_cuda()),
tm.timeout(60),
]
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.mgpu
def test_dask_training():
script = os.path.join(tm.demo_dir... |
from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .MarginMSELoss import MarginMSELoss
from .MSELoss import MSELoss
from .MultipleNegativesRa... | from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .MSELoss import MSELoss
from .MultipleNegativesRankingLoss import MultipleNegativesRanking... |
from typing import List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.indices.query.query_transform.base import BaseQueryTransform
from llama_index.core.prompts.mixin import PromptMixinType
from llama_index.cor... | from typing import List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.indices.query.query_transform.base import BaseQueryTransform
from llama_index.core.prompts.mixin import PromptMixinType
from llama_index.cor... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch. It generates sentence embeddings
that can be compared using cosine-similarity to measure the similarity.
Usage:
python training_stsbenchmark.py
OR
python training_stsbenchmark.py pretrained_... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch. It generates sentence embeddings
that can be compared using cosine-similarity to measure the similarity.
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer_model_... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import BBOX_SAMPLERS, build_sampler
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class CombinedSampler(BaseSampler):
"""A sampler that combines positive sampler and negative sampler."""
def __init__(self, pos_sampler, ne... | from ..builder import BBOX_SAMPLERS, build_sampler
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class CombinedSampler(BaseSampler):
"""A sampler that combines positive sampler and negative sampler."""
def __init__(self, pos_sampler, neg_sampler, **kwargs):
super(CombinedSamp... |
from docarray.document.mixins.proto import ProtoMixin
__all__ = ['ProtoMixin']
| from docarray.document.mixins.proto import ProtoMixin
|
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.add_... |
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from collections.abc import Mapping
from typing import Any, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic impo... | """Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from collections.abc import Mapping
from typing import Any, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic impo... |
from collections import defaultdict
import torch
import transforms as reference_transforms
def get_modules(use_v2):
# We need a protected import to avoid the V2 warning in case just V1 is used
if use_v2:
import torchvision.datapoints
import torchvision.transforms.v2
return torchvisio... | import torch
import transforms as T
class DetectionPresetTrain:
def __init__(self, *, data_augmentation, hflip_prob=0.5, mean=(123.0, 117.0, 104.0)):
if data_augmentation == "hflip":
self.transforms = T.Compose(
[
T.RandomHorizontalFlip(p=hflip_prob),
... |
from .CEBinaryAccuracyEvaluator import CEBinaryAccuracyEvaluator
from .CEBinaryClassificationEvaluator import CEBinaryClassificationEvaluator
from .CECorrelationEvaluator import CECorrelationEvaluator
from .CEF1Evaluator import CEF1Evaluator
from .CERerankingEvaluator import CERerankingEvaluator
from .CESoftmaxAccuracy... | from .CEBinaryAccuracyEvaluator import CEBinaryAccuracyEvaluator
from .CEBinaryClassificationEvaluator import CEBinaryClassificationEvaluator
from .CEF1Evaluator import CEF1Evaluator
from .CECorrelationEvaluator import CECorrelationEvaluator
from .CESoftmaxAccuracyEvaluator import CESoftmaxAccuracyEvaluator
from .CERer... |
#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
Run infe... | #!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
Run infe... |
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='dis... | # dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True... |
from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
from ._mask import Mask
from ._video import _TensorVideoType, _TensorVideoTypeJIT, _VideoType, ... | from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
from ._mask import Mask
from ._video import _TensorVideoType, _TensorVideoTypeJIT, _VideoType, ... |
from typing import Any, Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class CoSENTLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct=util.p... | from typing import Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class CoSENTLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct=util.pairwi... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestDy... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestDynamicRoIHead(TestCase):
def setUp(s... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmdet.registry import MODELS
from .utils import weighted_loss
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor)... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from mmdet.registry import MODELS
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Te... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Dict, Iterable, Optional
import spacy
from jina import DocumentArray, Executor, requests
_EXCLUDE_COMPONENTS = [
'tagger',
'parser',
'ner',
'senter',
'le... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Dict, Iterable, Optional
import spacy
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
_EXCLUDE_COMPONENTS = [
... |
from langchain_core.runnables.base import (
Other,
Runnable,
RunnableBinding,
RunnableBindingBase,
RunnableEach,
RunnableEachBase,
RunnableGenerator,
RunnableLambda,
RunnableLike,
RunnableParallel,
RunnableSequence,
RunnableSerializable,
coerce_to_runnable,
)
from lan... | from langchain_core.runnables.base import (
Other,
Runnable,
RunnableBinding,
RunnableBindingBase,
RunnableEach,
RunnableEachBase,
RunnableGenerator,
RunnableLambda,
RunnableLike,
RunnableParallel,
RunnableSequence,
RunnableSerializable,
coerce_to_runnable,
)
from lan... |
from typing import Union, Iterable, Dict
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this object to the o... | from typing import Union, Iterable
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this object to the other, ... |
import threading
from typing import Callable, ParamSpec, TypeVar
P = ParamSpec("P")
R = TypeVar("R")
def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
thread_local = threading.local()
def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
cache = getattr(thread_local, "cache", None)
i... | from typing import Callable, TypeVar, ParamSpec
import threading
P = ParamSpec("P")
R = TypeVar("R")
def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
thread_local = threading.local()
def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
cache = getattr(thread_local, "cache", None)
i... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.whylabs_callback import WhyLabsCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.whylabs_callback import WhyLabsCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... |
from __future__ import annotations
__version__ = "4.1.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
import warnings
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_mode... | from __future__ import annotations
__version__ = "4.1.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... |
_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
in... | _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
in... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, Iterable, Any
from jina import Executor, DocumentArray, requests
import torch
from .audio_clip.model import AudioCLIP
from .audio_clip.utils.transforms import ToTensor1D
class Aud... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, Iterable, Any
from jina import Executor, DocumentArray, requests
import torch
from model import AudioCLIP
from utils.transforms import ToTensor1D
class AudioCLIPEncoder(Executor):... |
import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationEventModel,
NotificationTypeOverride,
T_co,
)
from backen... | import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationEventModel,
NotificationTypeOverride,
T_co,
)
from backen... |
from __future__ import annotations
import numpy as np
from torch.utils.data import Dataset
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from sentence_transformers.readers.InputExample import InputExample
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEnc... | from typing import List
import numpy as np
from torch.utils.data import Dataset
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from sentence_transformers.readers.InputExample import InputExample
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import Any, ForwardRef, Optional, Union
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
from docarray.typing.tensor.abstract_tenso... |
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Imag... | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Imag... |
#!/usr/bin/env python
import distutils.command.clean
import os
import re
import shutil
import subprocess
from pathlib import Path
import torch
from setuptools import find_packages, setup
from tools import setup_helpers
ROOT_DIR = Path(__file__).parent.resolve()
def _run_cmd(cmd):
try:
return subprocess.... | #!/usr/bin/env python
import distutils.command.clean
import os
import re
import shutil
import subprocess
from pathlib import Path
import torch
from setuptools import find_packages, setup
from tools import setup_helpers
ROOT_DIR = Path(__file__).parent.resolve()
def _run_cmd(cmd):
try:
return subprocess.... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from numbers import Real
import numpy as np
from ..base import BaseEstimator, _fit_context
from ..utils._param_validation import Interval
from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
from ..utils.validation import chec... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from numbers import Real
import numpy as np
from ..base import BaseEstimator, _fit_context
from ..utils._param_validation import Interval
from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
from ..utils.validation import chec... |
"""**Tools** are classes that an Agent uses to interact with the world.
Each tool has a **description**. Agent uses the description to choose the right
tool for the job.
**Class hierarchy:**
.. code-block::
RunnableSerializable --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
... | """**Tools** are classes that an Agent uses to interact with the world.
Each tool has a **description**. Agent uses the description to choose the right
tool for the job.
**Class hierarchy:**
.. code-block::
RunnableSerializable --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
... |
"""Output parsers using Pydantic."""
import json
from typing import Annotated, Generic, Optional
import pydantic
from pydantic import SkipValidation
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import JsonOutputParser
from langc... | """Output parsers using Pydantic."""
import json
from typing import Annotated, Generic, Optional
import pydantic
from pydantic import SkipValidation
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import JsonOutputParser
from langc... |
from ._presets import StereoMatching # usort: skip
from ._augment import RandomCutmix, RandomMixup, SimpleCopyPaste
from ._geometry import FixedSizeCrop
from ._misc import PermuteDimensions, TransposeDimensions
from ._type_conversion import LabelToOneHot
| from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._presets import StereoMatching # usort: skip
from ._augment import RandomCutmix, RandomErasing, RandomMixup, SimpleCopyPaste
fr... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet.core.bbox import distance2bbox
from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
from mmdet.core.utils import mask2ndarray
def dummy_raw_polygon_masks(size):
"""
Args:
size (tuple):... | import numpy as np
import pytest
import torch
from mmdet.core.bbox import distance2bbox
from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
from mmdet.core.utils import mask2ndarray
def dummy_raw_polygon_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (N, H, W)
R... |
"""
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... | """
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... |
_base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=m... | _base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
"""
Functions for building sdist
"""
import logging
import pathlib
from .util import copy_with_logging, copytree_with_logging
def copy_cpp_src_tree(
cpp_src_dir: pathlib.Path, target_dir: pathlib.Path, logger: logging.Logger
) -> None:
"""Copy C++ source tree into build directory"""
for subdir in [
... | """
Functions for building sdist
"""
import logging
import pathlib
from .util import copy_with_logging, copytree_with_logging
def copy_cpp_src_tree(
cpp_src_dir: pathlib.Path, target_dir: pathlib.Path, logger: logging.Logger
) -> None:
"""Copy C++ source tree into build directory"""
for subdir in [
... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=d... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=d... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
from pathlib import Path
from typing import Dict
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from PIL import Image
from torchvision.models.mobile... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from typing import Dict
import pytest
import torch
import numpy as np
from torchvision.models.mobilenetv2 import model_urls
from PIL import Image
from jina import DocumentArray, Document
@pytest.fixture(... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.